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swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes

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  1. README.md +25 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.78
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes](https://huggingface.co/NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5073
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- - Accuracy: 0.78
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  ## Model description
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@@ -61,22 +61,31 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 10
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | No log | 0.9231 | 3 | 0.5972 | 0.78 |
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- | No log | 1.8462 | 6 | 0.9760 | 0.78 |
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- | No log | 2.7692 | 9 | 0.7597 | 0.78 |
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- | 0.5361 | 4.0 | 13 | 0.5870 | 0.78 |
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- | 0.5361 | 4.9231 | 16 | 0.5333 | 0.78 |
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- | 0.5361 | 5.8462 | 19 | 0.5040 | 0.78 |
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- | 0.4032 | 6.7692 | 22 | 0.4990 | 0.78 |
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- | 0.4032 | 8.0 | 26 | 0.5073 | 0.78 |
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- | 0.4032 | 8.9231 | 29 | 0.5070 | 0.78 |
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- | 0.3579 | 9.2308 | 30 | 0.5073 | 0.78 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.83
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes](https://huggingface.co/NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4176
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+ - Accuracy: 0.83
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 0.9231 | 3 | 0.4943 | 0.78 |
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+ | No log | 1.8462 | 6 | 0.4716 | 0.78 |
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+ | No log | 2.7692 | 9 | 0.4725 | 0.81 |
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+ | 0.3732 | 4.0 | 13 | 0.4678 | 0.78 |
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+ | 0.3732 | 4.9231 | 16 | 0.4779 | 0.78 |
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+ | 0.3732 | 5.8462 | 19 | 0.4564 | 0.79 |
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+ | 0.3459 | 6.7692 | 22 | 0.4556 | 0.82 |
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+ | 0.3459 | 8.0 | 26 | 0.4757 | 0.77 |
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+ | 0.3459 | 8.9231 | 29 | 0.4773 | 0.77 |
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+ | 0.3273 | 9.8462 | 32 | 0.4661 | 0.77 |
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+ | 0.3273 | 10.7692 | 35 | 0.4518 | 0.79 |
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+ | 0.3273 | 12.0 | 39 | 0.4405 | 0.81 |
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+ | 0.2974 | 12.9231 | 42 | 0.4359 | 0.82 |
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+ | 0.2974 | 13.8462 | 45 | 0.4298 | 0.82 |
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+ | 0.2974 | 14.7692 | 48 | 0.4242 | 0.84 |
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+ | 0.2874 | 16.0 | 52 | 0.4199 | 0.84 |
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+ | 0.2874 | 16.9231 | 55 | 0.4185 | 0.83 |
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+ | 0.2874 | 17.8462 | 58 | 0.4179 | 0.83 |
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+ | 0.2737 | 18.4615 | 60 | 0.4176 | 0.83 |
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  ### Framework versions
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